Introduction to AI and Machine Learning

About

This summer course, tailored for beginners from diverse fields, simplifies the complexities of artificial intelligence (AI) and machine learning (ML) through engaging, hands-on experiences and real-world applications. With a special focus on Natural Language Processing, you will gain practical skills and insights to navigate the world of AI on a deeper level.

You will explore the fundamental principles of AI and ML, learn about data preprocessing techniques, and understand the architecture and functionality of Large Language Models (LLMs) like BERT and GPT. During the course, you will also engage in practical activities, such as building sentiment analysis models, fine-tuning pre-trained LLMs, and deploying them in various applications, including chatbots and text generation tools.

The course will also cover the ethical considerations and challenges associated with LLMs, providing a comprehensive understanding of these powerful models' potential and limitations.

This workshop-based summer course is ideal if you are looking to gain practical knowledge and experience in AI and ML, with a particular emphasis on the transformative capabilities of large language models. By the end of the course, you will have a strong foundation in AI and machine learning. You will also be equipped with the skills to apply large language models to solve real-world challenges and develop innovative solutions within your respective fields.

Course leader

Doctor of Science (SDr.) Dariush Salami is a distinguished Radio Research Scientist at Nokia Bell Labs. He has deep expertise in AI and machine learning for wireless networks, including cutting-edge 5G and 6G technologies. He earned his PhD from Aalto University as a Marie Skłodowska Curie fellow, focusing on human-centric sensing using mmWave radars. Salami has extensive industrial experience, notably serving as the CTO of Rectified.ai, a platform-agnostic automatic machine-learning solution. He has lectured the Machine Learning D course for nearly 1000 students, making it the largest AI/ML course in Finland. His innovative work is recognised through several patents and numerous publications in prestigious journals. Additionally, he has secured multiple grants, including from the Nokia Foundation and Google, underscoring his contributions to advancing technology.

Target group

The course does not require any specific prerequisites. It is ideal for individuals from diverse backgrounds, including students, professionals, and enthusiasts looking to enhance their understanding of artificial intelligence and machine learning, particularly in the context of Large Language Models. Those in fields such as engineering, data science, software development, and business will benefit from the practical skills gained in applying AI techniques to solve real-world problems. The workshop also equips participants with tools to navigate the rapidly evolving AI landscape.

Participants must have completed a high school or vocational degree or equivalent by the time the course starts. However, they do not have to be a degree student at a university to participate in our courses.

Additionally, all summer school course students must be 18 years or older, as it is the legal age in Finland.

Course aim

Understand core concepts

Learn the fundamentals of AI and ML with a focus on large language models (LLMs).
Explore the architecture and functionality of models like BERT and GPT.

Preprocess text data

Perform tokenisation, stemming, lemmatisation, stop-word removal, and vectorisation.
Use tools like NLTK and SpaCy to clean and prepare text data.

Apply machine learning to NLP and develop NLP applications

Implement supervised learning for tasks like sentiment analysis and text classification.
Fine-tune pre-trained LLMs using frameworks like Hugging Face’s Transformers.
Build and deploy language generation tools such as chatbots and text generators.

Analyse and mitigate ethical concerns

Identify ethical challenges like bias and data privacy in LLMs.
Develop strategies to reduce bias and promote responsible AI use.

Deploy and integrate models

Deploy AI/ML models on cloud services and edge devices.
Implement end-to-end solutions showcasing LLMs in practical applications.

Engage in hands-on projects

Collaborate on a real-world NLP project using LLMs.
Present project outcomes, demonstrating real-world impact and innovation.

Fee info